Current Trends in Fetal Cardiology: Results from an International Benchmarking Survey of Fetal Cardiac Programs Through the Fetal Heart Society Research Collaborative
Bibliographic record
Abstract
Fetal cardiology has grown into a robust pediatric cardiology subspecialty in the last two decades, with many congenital heart centers having a dedicated fetal cardiac program. Despite the subspeciality's clear maturation, there are no multicenter data to describe volume, practice patterns, program structures, resources, or trends. The Fetal Heart Society sought to address this deficiency by conducting an international survey of fetal cardiac programs. A survey was distributed internationally to fetal cardiac programs. One response per institution or clinical practice was collected. Respondents were asked to provide data from the 2022 calendar year. Ninety-five programs responded, with the majority representing the United States of America (n = 75, 79%) or Canada (n = 11, 12%). Most responding programs (88%) had an academic or university affiliation, and 69% were from independent children's hospitals. The median number of fetal echocardiographic studies performed annually per program was 1,000 (IQR 580-2,100). There was a median of 5 (IQR 3-7) fetal cardiologists and 5 (IQR 3-7) sonographers per program. Each fetal cardiologist interpreted an estimated median of 6 (IQR 5-8) fetal studies in a full day shift. The most common duration allocated for each fetal echocardiographic study was 45-59 min for the initial study (51%) and 45-59 min for the follow-up study (42%). 79% of programs had a fetal cardiac nurse coordinator. An independent fetal database was maintained at 78% of programs. Less than half of programs (46/95) had a formal quality improvement (QI) initiative, with only 22 programs participating in national-level QI metrics. The most frequently reported barrier to having a fetal cardiac QI program was a lack of human resources (60%), followed by a lack of institutional support/incentive (41%). Programs were more likely to have a formal fetal cardiology QI program if they had a fetal cardiac coordinator (p = 0.0029) if they had a formal fetal database (p = 0.003), or if they were a larger volume program (p = 0.026). Certain subspecialties were available at most programs, including neonatology (93%), maternal-fetal medicine (88%), genetic counseling (88%), and social work (79%). However, psychology (38%) and psychiatry (16%) services to address parental mental health issues were not as commonly available. These survey data provide a novel and comprehensive view of fetal cardiology programs with information useful for internal benchmarking, quality improvement initiatives, resource allocation, and identifying unmet needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".